Summary of Contextualstory: Consistent Visual Storytelling with Spatially-enhanced and Storyline Context, by Sixiao Zheng et al.
ContextualStory: Consistent Visual Storytelling with Spatially-Enhanced and Storyline Context
by Sixiao Zheng, Yanwei Fu
First submitted to arxiv on: 13 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes ContextualStory, a novel framework for visual storytelling that addresses limitations in existing autoregressive methods. The model utilizes Spatially-Enhanced Temporal Attention to capture spatial and temporal dependencies, handling significant character movements effectively. Additionally, the Storyline Contextualizer enriches context in storyline embedding, and the StoryFlow Adapter measures scene changes between frames for guiding the model. Experimental results on PororoSV and FlintstonesSV datasets demonstrate that ContextualStory outperforms existing state-of-the-art methods in both story visualization and continuation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us tell stories with pictures! It’s hard to make a movie or animation because you need lots of frames that look like they belong together. Right now, computers can’t do this very well. The new system called ContextualStory is better at making movies and animations by understanding what’s happening in each frame and how it relates to the others. |
Keywords
» Artificial intelligence » Attention » Autoregressive » Embedding
